Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis
Abstract
Objective: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. Background: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. Method: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. Results: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. Conclusion: Effects of cognitive load on driving are strongly selective and task dependent. Application: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.
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The development of automatic processing is a critical feature of much complex skill learning, with driving being a primary example.
“The development of automatic processing is a critical feature of much complex skill learning, with driving being a primary example. 27”
From Domains of cognition and their assessment (2019) · cites this paper as [27]
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Cited by
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- Authors
- Engström, J., Markkula, G., Victor, T., & Merat, N.
- Affiliations
- Virginia Tech Transportation Institute (US)
- University of Leeds (GB)
- Volvo (Sweden) (SE)
- Corresponding author
- Engström, Johan
- Venue
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Publisher
- SAGE Publications
- Published
- 2017-02-10
- Volume
- 59
- Issue
- 5
- Pages
- 734-764
- ISSN
- 0018-7208 (ISSN-L)
- 1547-8181
- OpenAlex
- W2586996035
- Cited by
- 299
- References
- 147
- Citations by year
- 2026: 50 · 2025: 53 · 2024: 39 · 2023: 23 · 2022: 23 · 2021: 35 · 2020: 34 · 2019: 19 · 2018: 15 · 2017: 8
- FWCI
- 29.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/J002933/1)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Keywords
- cognitive load, driving performance, driver attention, task automation, selective impairment, dual-task driving, driver distraction, crash causation
- MeSH terms
- Adult, Attention, Automobile Driving, Female, Humans, Male, Memory, Short-Term, Psychomotor Performance, Executive Function
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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